Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
Embodiments relate to data processing, and in particular, to systems and methods employing automatic learning and/or optimization of sequences.
Sequences of items arranged in an order, are frequently encountered in ordinary life. One example is a sequence in which items are ticked off of a grocery list. Other examples of repeated sequences may be an order in which emails are read upon opening up an in-box, and a sequence of activities to complete a project.
Typically, upon encountering a familiar situation (e.g., grocery trip, in-box opening, repeat project assignment), an individual will simply manually create a relevant sequence based upon past experience and intuition. Such an informal approach to sequence building, however, may not be optimal.
In particular, the manual exercise of discretion to create a sequence may not be reproducible. Moreover, such manual action may leverage off of a limited pool of information (e.g., in the user's memory), when in fact substantial additional relevant contextual information may be available to the user.